ML

ML: Multilevel Preconditioning Package. Welcome to the homepages for ML, Sandia’s main multigrid preconditioning package. ML is designed to solve large sparse linear systems of equations arising primarily from elliptic PDE discretizations. ML is used to define and build multigrid solvers and preconditioners, and it contains black-box classes to construct highly-scalable smoothed aggregation preconditioners. ML preconditioners have been used on thousands of processors for a variety of problems, including the incompressible Navier-Stokes equations with heat and mass transfer, linear and nonlinear elasticity equations, the Maxwell equations, semiconductor equations, and more.


References in zbMATH (referenced in 81 articles )

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  1. Augustin, Christoph M.; Neic, Aurel; Liebmann, Manfred; Prassl, Anton J.; Niederer, Steven A.; Haase, Gundolf; Plank, Gernot: Anatomically accurate high resolution modeling of human whole heart electromechanics: A strongly scalable algebraic multigrid solver method for nonlinear deformation (2016)
  2. Ballard, Grey; Siefert, Christopher; Hu, Jonathan: Reducing communication costs for sparse matrix multiplication within algebraic multigrid (2016)
  3. Berger-Vergiat, Luc; McAuliffe, Colin; Waisman, Haim: Parallel preconditioners for monolithic solution of shear bands (2016)
  4. Chidyagwai, Prince; Ladenheim, Scott; Szyld, Daniel B.: Constraint preconditioning for the coupled Stokes-Darcy system (2016)
  5. Cyr, Eric C.; Shadid, John N.; Tuminaro, Raymond S.: Teko: a block preconditioning capability with concrete example applications in Navier-Stokes and MHD (2016)
  6. D’Elia, Marta; Ridzal, Denis; Peterson, Kara J.; Bochev, Pavel; Shashkov, Mikhail: Optimization-based mesh correction with volume and convexity constraints (2016)
  7. Gholami, Amir; Malhotra, Dhairya; Sundar, Hari; Biros, George: FFT, FMM, or multigrid? A comparative study of state-of-the-art Poisson solvers for uniform and nonuniform grids in the unit cube (2016)
  8. Hamilton, Steven; Berrill, Mark; Clarno, Kevin; Pawlowski, Roger; Toth, Alex; Kelley, C.T.; Evans, Thomas; Philip, Bobby: An assessment of coupling algorithms for nuclear reactor core physics simulations (2016)
  9. Møyner, Olav; Lie, Knut-Andreas: A multiscale restriction-smoothed basis method for high contrast porous media represented on unstructured grids (2016)
  10. Newman, Christopher; Womeldorff, Geoffrey; Knoll, Dana A.; Chacón, Luis: A communication-avoiding implicit-explicit method for a free-surface ocean model (2016)
  11. Raghunathan, Ram; Muller, Stefan K.; Acar, Umut A.; Blelloch, Guy: Hierarchical memory management for parallel programs (2016)
  12. Shadid, J.N.; Smith, T.M.; Cyr, E.C.; Wildey, T.M.; Pawlowski, R.P.: Stabilized FE simulation of prototype thermal-hydraulics problems with integrated adjoint-based capabilities (2016)
  13. Stoll, Martin; Pearson, John W.; Maini, Philip K.: Fast solvers for optimal control problems from pattern formation (2016)
  14. Tan, Yong Kiam; Myreen, Magnus O.; Kumar, Ramana; Fox, Anthony; Owens, Scott; Norrish, Michael: A new verified compiler backend for CakeML (2016)
  15. Waluga, Christian; Wohlmuth, Barbara; Rüde, Ulrich: Mass-corrections for the conservative coupling of flow and transport on collocated meshes (2016)
  16. Hamilton, Steven P.; Evans, Thomas M.: Efficient solution of the simplified $P_N$ equations (2015)
  17. Notay, Yvan; Napov, Artem: A massively parallel solver for discrete Poisson-like problems (2015)
  18. Rhebergen, Sander; Wells, Garth N.; Wathen, Andrew J.; Katz, Richard F.: Three-field block preconditioners for models of coupled magma/mantle dynamics (2015)
  19. Badia, Santiago; Martín, Alberto F.; Principe, Javier: A highly scalable parallel implementation of balancing domain decomposition by constraints (2014)
  20. Bosch, Jessica; Stoll, Martin; Benner, Peter: Fast solution of Cahn-Hilliard variational inequalities using implicit time discretization and finite elements (2014)

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